Abstract: Desert seismic signals contain strong random noise，which brings great trouble to the processing and interpretation of desert seismic signals． In order to solve this technical problem，Deep Ｒesidual Convolutional Neural Network for Shearlet Transform model is proposed for the implementation of the desert seismic signal random noise suppression． In training phase，the Shearlet coefficients of desert seismic data are taken as inputs，and the Shearlet coefficients of random noise are taken as labels． Through network training，the mapping relationship between them could be learned by a deep CNN ( Convolutional Neural Network) ． In test phase，the coefficients of random noise can be predicted from the coefficients of desert seismic data by the mapping relationship，and thereafter the effective signals coefficients is obtained indirectly． Finally the effective signals can be reconstructed by inverse Shearlet transform． By comparing with the traditional Shearlet hard threshold denoising algorithm，the proposed algorithm has improved the SNＲ of the desert seismic signals from － 4． 48 dB to 14． 15 dB and has better denoising performance．

Key words: desert seismic signals, noise suppression, Shearlet transform, deep residual convolutional neural network